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• Part 1: What is Coincidence Analysis?

  1. Module 1: Why CNA exists
  2. Module 2: What CNA does
  3. Module 3: Three core ideas

• Part 2: Why not just use regression?

  1. Module 4: Why not regression?

• Part 3: Where does CNA apply?

  1. Module 5: Research questions
  2. Module 6: Cases

• Part 4: How do you design a study?

  1. Module 7: Planning a study
  2. Module 8: Conceptual framework

• Part 5: How do you prepare the data?

  1. Module 9: Data requirements
  2. Module 10: Supervision dataset
  3. Module 11: Data quality

• Part 6: How do you read a CNA result?

  1. Module 12: Reading CNA notation

• Part 7: How do you learn R from zero?

  1. Module 13: Install R and RStudio
  2. Module 14: First R commands
  3. Module 15: Install the cna package

• Part 8: How do you build your first CNA model?

  1. Module 16: Create the dataset
  2. Module 17: Run CNA
  3. Module 18: Read the result
  4. Module 19: Logic symbols

• Part 9: How does CNA work on real problems?

  1. Module 20: Low DTP3 coverage
  2. Module 21: Zero-dose children
  3. Module 22: Supervision quality

• Part 10: How do you evaluate a model?

  1. Module 23: Consistency
  2. Module 24: Coverage score
  3. Module 25: Comparing models

• Part 11: How robust is your finding?

  1. Module 26: Robustness
  2. Module 27: Sensitivity analysis

• Part 12: How do you interpret the results?

  1. Module 28: Plain-language findings
  2. Module 29: Common mistakes

• Part 13: How do you write it up?

  1. Module 30: Results template
  2. Module 31: Policy brief
  3. Module 32: Executive summary

• Part 14: Can you run a full study?

  1. Capstone project
  2. Final competencies